Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	141,069
2	Rhode Island	139,882
3	South Dakota	138,652
4	Utah	123,952
5	Tennessee	121,955
6	Arizona	118,495
7	Iowa	115,636
8	Nebraska	113,639
9	Wisconsin	113,468
10	Oklahoma	113,294
11	South Carolina	112,389
12	New Jersey	112,272
13	Arkansas	111,248
14	Alabama	107,669
15	Indiana	107,504
16	Delaware	107,217
17	Kansas	106,824
18	Illinois	105,642
19	New York	105,190
20	Idaho	105,047
21	Mississippi	104,799
22	Florida	103,991
23	Nevada	102,409
24	Minnesota	102,117
25	Montana	101,921
26	Georgia	101,327
27	Wyoming	100,459
28	Kentucky	100,157
29	Massachusetts	99,959
30	Texas	99,806
31	Louisiana	98,645
32	Missouri	97,896
33	Connecticut	95,148
34	California	94,706
35	New Mexico	94,300
36	Michigan	93,701
37	North Carolina	93,143
38	Alaska	92,404
39	Ohio	91,736
40	Pennsylvania	90,317
41	Colorado	89,212
42	West Virginia	85,443
43	Virginia	77,275
44	Maryland	74,034
45	New Hampshire	69,783
46	District of Columbia	67,583
47	Washington	53,181
48	Puerto Rico	51,680
49	Maine	45,538
50	Oregon	43,823
51	Vermont	36,782
52	Hawaii	23,142

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Michigan	430
2	Colorado	326
3	Minnesota	313
4	Rhode Island	303
5	Pennsylvania	295
6	Delaware	274
7	Florida	250
8	Illinois	243
9	West Virginia	238
10	New Jersey	233
11	Alaska	225
12	Oregon	221
13	Massachusetts	210
14	North Dakota	207
15	Indiana	204
16	Washington	200
17	North Carolina	196
18	Maine	194
19	New York	182
20	Connecticut	176
21	South Carolina	174
22	New Hampshire	167
23	Kentucky	164
24	Montana	164
25	South Dakota	162
26	Maryland	160
27	Tennessee	158
28	Wyoming	149
29	Vermont	147
30	Wisconsin	147
31	Nevada	146
32	Puerto Rico	146
33	Ohio	144
34	Iowa	138
35	Virginia	138
36	Kansas	137
37	Utah	137
38	Georgia	133
39	Texas	131
40	Missouri	126
41	Nebraska	126
42	Louisiana	121
43	Idaho	116
44	New Mexico	116
45	Arizona	106
46	District of Columbia	106
47	Hawaii	87
48	Alabama	82
49	Mississippi	82
50	Arkansas	80
51	Oklahoma	76
52	California	47

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	2,876
2	New York	2,659
3	Massachusetts	2,554
4	Rhode Island	2,521
5	Mississippi	2,418
6	Arizona	2,380
7	Connecticut	2,271
8	Louisiana	2,233
9	South Dakota	2,223
10	Alabama	2,222
11	Pennsylvania	2,052
12	North Dakota	1,998
13	Indiana	1,980
14	New Mexico	1,940
15	Illinois	1,916
16	Arkansas	1,901
17	Iowa	1,885
18	Michigan	1,876
19	South Carolina	1,844
20	Georgia	1,842
21	Nevada	1,773
22	Tennessee	1,771
23	Texas	1,738
24	Kansas	1,724
25	Oklahoma	1,715
26	Delaware	1,668
27	Ohio	1,649
28	Florida	1,637
29	District of Columbia	1,565
30	California	1,562
31	Missouri	1,506
32	West Virginia	1,494
33	Kentucky	1,481
34	Montana	1,470
35	Maryland	1,446
36	Wisconsin	1,296
37	Minnesota	1,283
38	Virginia	1,261
39	Nebraska	1,223
40	Wyoming	1,221
41	North Carolina	1,208
42	Idaho	1,146
43	Colorado	1,116
44	New Hampshire	958
45	Washington	728
46	Puerto Rico	721
47	Utah	686
48	Oregon	595
49	Maine	583
50	Alaska	451
51	Vermont	394
52	Hawaii	339

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Michigan	6
2	Oklahoma	6
3	Georgia	4
4	Minnesota	4
5	New Mexico	4
6	Pennsylvania	4
7	South Carolina	4
8	Florida	3
9	Illinois	3
10	Maryland	3
11	New Jersey	3
12	West Virginia	3
13	Wisconsin	3
14	Alabama	2
15	Arizona	2
16	California	2
17	Connecticut	2
18	Delaware	2
19	District of Columbia	2
20	Iowa	2
21	Louisiana	2
22	Massachusetts	2
23	Nevada	2
24	New Hampshire	2
25	New York	2
26	North Carolina	2
27	North Dakota	2
28	Ohio	2
29	Puerto Rico	2
30	Rhode Island	2
31	South Dakota	2
32	Texas	2
33	Arkansas	1
34	Colorado	1
35	Indiana	1
36	Kansas	1
37	Kentucky	1
38	Maine	1
39	Mississippi	1
40	Missouri	1
41	Montana	1
42	Tennessee	1
43	Utah	1
44	Virginia	1
45	Washington	1
46	Alaska	0
47	Hawaii	0
48	Idaho	0
49	Nebraska	0
50	Oregon	0
51	Vermont	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Chattahoochee	Georgia	361,511	1	99
Crowley	Colorado	358,687	2	99
Bent	Colorado	272,907	3	99
Dewey	South Dakota	248,133	4	99
Lincoln	Arkansas	245,700	5	99
Davidson	Tennessee	141,445	186	94
Richland	South Carolina	110,489	995	68
York	South Carolina	108,812	1060	66
Orange	California	85,053	2176	30
Pierce	Washington	54,198	2874	8

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Foard	Texas	8,658	1	99
Galax city	Virginia	8,035	2	99
Jerauld	South Dakota	7,948	3	99
Emporia city	Virginia	7,856	4	99
Hancock	Georgia	7,686	5	99
Orange	California	1,561	1821	42
York	South Carolina	1,342	2075	33
Davidson	Tennessee	1,341	2077	33
Richland	South Carolina	1,311	2111	32
Pierce	Washington	717	2741	12

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.